Updating README.md

README.md

@@ -16,7 +16,7 @@ datasets:
 <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
 
 This is one of the encoder-only monolingual language models trained as a first release by the [HPLT project](https://hplt-project.org/).
-It is a so called masked language models. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
+It is a so called masked language model. In particular, we used the modification of the classic BERT model named [LTG-BERT](https://aclanthology.org/2023.findings-eacl.146/).
 
 A monolingual LTG-BERT model is trained for every major language in the [HPLT 1.2 data release](https://hplt-project.org/datasets/v1.2) (*75* models total).
 
@@ -55,15 +55,54 @@ print(tokenizer.decode(output_text[0].tolist()))
 
 The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
 
+## Intermediate checkpoints
+
+We are releasing 10 intermediate checkpoints for each model at intervals of every 3125 training steps in separate branches. The naming convention is `stepXXX`: for example, `step18750`.
+
+You can load a specific model revision with `transformers` using the argument `revision`:
+```python
+model = AutoModelForMaskedLM.from_pretrained("HPLT/hplt_bert_base_en", revision="step21875", trust_remote_code=True)
+```
+
+You can access all the revisions for the models with the following code:
+```python
+from huggingface_hub import list_repo_refs
+out = list_repo_refs("HPLT/hplt_bert_base_en")
+print([b.name for b in out.branches])
+```
+
 ## Cite us
 
 ```bibtex
-@misc{degibert2024new,
-      title={A New Massive Multilingual Dataset for High-Performance Language Technologies}, 
-      author={Ona de Gibert and Graeme Nail and Nikolay Arefyev and Marta Bañón and Jelmer van der Linde and Shaoxiong Ji and Jaume Zaragoza-Bernabeu and Mikko Aulamo and Gema Ramírez-Sánchez and Andrey Kutuzov and Sampo Pyysalo and Stephan Oepen and Jörg Tiedemann},
-      year={2024},
-      eprint={2403.14009},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+@inproceedings{de-gibert-etal-2024-new-massive,
+    title = "A New Massive Multilingual Dataset for High-Performance Language Technologies",
+    author = {de Gibert, Ona  and
+      Nail, Graeme  and
+      Arefyev, Nikolay  and
+      Ba{\~n}{\'o}n, Marta  and
+      van der Linde, Jelmer  and
+      Ji, Shaoxiong  and
+      Zaragoza-Bernabeu, Jaume  and
+      Aulamo, Mikko  and
+      Ram{\'\i}rez-S{\'a}nchez, Gema  and
+      Kutuzov, Andrey  and
+      Pyysalo, Sampo  and
+      Oepen, Stephan  and
+      Tiedemann, J{\"o}rg},
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.100",
+    pages = "1116--1128",
+    abstract = "We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and processing of large corpora, which rely on open-source software tools and high-performance computing. Our monolingual collection focuses on low- to medium-resourced languages and covers 75 languages and a total of {\mbox{$\approx$}} 5.6 trillion word tokens de-duplicated on the document level. Our English-centric parallel corpus is derived from its monolingual counterpart and covers 18 language pairs and more than 96 million aligned sentence pairs with roughly 1.4 billion English tokens. The HPLT language resources are one of the largest open text corpora ever released, providing a great resource for language modeling and machine translation training. We publicly release the corpora, the software, and the tools used in this work.",
 }
-```
+```
+